Overall, the above results are encouraging given the faster inference time of ForceNet compared to DimeNet++.
However, the results also suggest a potential limitation of ForceNet's force-centric approach: the superior performance of ForceNet-large over DimeNet++ in the S2F task (i.e., estimate the forces of 3D structures along the simulation trajectory) does not directly translate into its superior performance on the IS2RS simulation task.
We deduce this is due to the compounding error problem of the force-centric approach pointed out by the GNS work [1]}, i.e., model's prediction errors accumulate along the simulation trajectory, which forces the model to make increasingly erroneous prediction over structures that are far away from the simulation trajectory.
The energy-centric models may suffer less from the problem since their built-in physical constraints allow them to make more well-behaved force prediction over the off-trajectory structures, which eventually leads to better simulation results despite the worse force prediction results.
